Development of a kidney cancer <i>ex vivo t</i>umor model and image-based artificial intelligence tool for precision immunotherapy and combination therapy testing.
Authors: Eleonora Peerani, Jay Kearney, Demi Annemarie Wiskerke, Thomas David Laurent Richardson, Gastón Augustin Primo, Keqian Nan, Francesco Iori, Elli Tham, George Richard Tiger Bevan de Fraine, Aston Martin Crawley, Chandan Seth Nanda, Matthew Williams, Maxine Gia Binh Tran, Duleek Nimantha Bandara Ranatunga
Journal: Journal of Clinical Oncology (2023)

e16525 Background: Immuno-oncology (IO) initiated a major shift in the cancer treatment paradigm, as therapies no longer target cancer cells directly but rather restore and exploit a patient’s own antitumor immunity. However at present, there are no conclusive diagnostic tools capable of predicting combination immunotherapy response with high accuracy in renal cancer. We’ve developed an ex vivo IO model and multivariate analysis to predict clinical drug efficacy by combining patient tumor samples, functional assays, artificial intelligence and omics. This model recapitulates each patient’s tumor-immune microenvironment (TiME) and allows time-course bulk and single-cell resolution analyses of functional metrics in 3D. Methods: Human renal tumor resections and matched blood (N=20 REC approved) were processed for spatial transcriptomics and single cell isolation of tumor and PBMCs. Tumor-dissociated (tumor, stromal, immune, etc.) cells and PBMC subsets were characterized by flow cytometry (FCm). Target cells (tumor) and effector cells (PBMCs and subsets thereof, CD8+) were stained with different fluorescent dyes including viability probes (caspase 3/7, SYTOX) and encapsulated in hydrogels that recapitulate human TiME physiology. Cultures were treated with approved regimens including immune checkpoint (ipilimumab, pembrolizumab) and receptor-tyrosine kinase (TKI)(cabozantinib, lenvatinib) inhibitors. Cells were tracked over 7 days using 3D time-course confocal microscopy. Computer vision analysis detected and quantified behaviors such as immune infiltration, immune/tumor cell migration, T cell-mediated tumor killing and tumor viability in response to treatments. Results: Our preliminary data shows that treatment with pembrolizumab resulted in a 26% increase in the infiltration of CD8- PBMC fraction into the microtumor core and a 14% increase in infiltration of the CD8+ subset. Tumor cell death was 15% higher in pembro-treated samples compared to untreated and 30% higher compared to tumor cultures alone (no PBMCs). Migration speed of immune cells was found to be higher as cells invaded and slower through engagement/killing, peaking at day 1 (3 µm/min) and slowing to 2 and 1.5 µm/min for CD8+ and CD8- cells by day 3. FCm was used to characterize tumor cell subpopulations (cancer, endothelial, immune) and the expression of targets in each patient. Treatment with TKIs led to reduced phosphorylation of VEGFR, PDGFRβ and HGFR (N=3). Conclusions: Our platform allows time-course analysis of functional cell response metrics. The model tests treatment combinations across multiple modes of action and quantifies cell response including viability, death, migration, infiltration and immune-surveillance. Future work aims to further develop the platform to match/predict patient responses in breast (NCT05435352), kidney, and other tumors.